As enterprises increasingly rely upon data networks to manage content, business processes, and share information, many enterprises want users to be able to find useful data and documents in a reliable manner. Thus, it is importance that there is a high relevance between the document or data properties that a user is seeking and the results of a content search. Additionally, it is important that a search does not return so much data such that the users cannot find the desired data or document.
One type of search that is performed by in enterprise systems is a keyword search in which a user enters relevant topics to initiate the search. Unfortunately, many keyword searches return such a volume of documents containing the keywords that the search results may not be useful. In other words, so much data is returned that the search initiator cannot find the desired data or document.
A metadata search is frequently more useful for finding data or documents where there is some prior knowledge of the content type. For example, an enterprise may have a customer relations database, a marketing research section, a library, a supply chain section, an accounts section, etc. Each of these departments comprises a known content type in which the properties of the data are known in advance. However, because each of these collections of data has different content types, a single metadata search solution may have limited utility. As a result, it is often desirable to create a different metadata search interface for each content type in an enterprise. However, it is often time consuming to develop a customized search solution in addition to creating the user interface (UI) for each content type.